GAMES : Virtual Worlds and Reality Selected Papers of ISAGA 2008

Bulletin of the American Meteorological Society, 2019

Climate predictions, from three weeks to a decade into the future, can provide invaluable information for climate-sensitive socioeconomic sectors, such as renewable energy, agriculture, or insurance. However, communicating and interpreting these predictions is not straightforward. Barriers hindering user uptake include a terminology gap between climate scientists and users, the difficulties of dealing with probabilistic outcomes for decision-making, and the lower skill of climate predictions compared to the skill of weather forecasts. This paper presents a gaming approach to break communication and understanding barriers through the application of the Weather Roulette conceptual framework. In the game, the player can choose between two forecast options, one that uses ECMWF seasonal predictions against one using climatology-derived probabilities. For each forecast option, the bet is spread proportionally to the predicted probabilities, either in a single year game or a game for the w...

Methods for handling uncertainty should be evaluated in terms of their cognitive compatibility with real-world decision makers. Bayesian models of uncertainty demand precise up-front assessments of all problem elements and discourage the dynamic evolution of problem understanding. They handle missing or conflicting data by mathematical aggregation, while realworld decision makers regard gaps in knowledge and conflicting evidence as problems to be solved. Finally, they produce as output a statistical average rather than a coherent picture of the situation. Another approach to decision making, based on pattern-matching, does not address the ways in which situation pictures are evaluated and modified. A third approach, however, called the Recognition / Metacognition model, treats decision making under uncertainty as a problem-solving process that starts with the results of recognition, verifies them, and improves them where necessary. Critiquing strategies identify problems of incompleteness, conflict, and unreliability in situation models, and lead to correcting steps that retrieve or collect additional information or adopt assumptions. Training methods based on this model have been developed and tested with activeduty Naval officers.

Journal of Experimental Psychology-human Perception and Performance, 1988

A two-stage within subjects design was used to compare decisions based on numerically and verbally expressed probabilities. In Stage 1, subjects determined approximate equivalences between vague probability expressions, numerical probabilities, and graphical displays. Subsequently, in Stage 2 they bid for (Experiment 1J or rated (Experiment 2) gambles based on the previously equated verbal, numerical, and graphical descriptors. In Stage 1, numerical and verbal judgments were reliable, internally consistent, and monotonically related to the displayed probabilities. However, the numerical judgments were significantly superior in all respects because they were much less variable within and between subjects. In Stage 2, response times, bids, and ratings were inconsistent with both of two opposing sets of predictions, one assuming that imprecise gambles will be avoided and the other that verbal probabilities will be preferred. The entire pattern of results is explained by means of a general model of decision making with vague probabilities which assumes that in the present task, when presented with a vague probability word, people focus on an implied probability interval and sample values within it to resolve the vagueness prior to forming a bid or a rating.

2018

We experimentally explore decision-making under uncertainty using a framework that decomposes uncertainty into three distinct layers: (1) physical uncertainty, entailing inherent randomness within a given probability model, (2) model uncertainty, entailing subjective uncertainty about the probability model to be used and (3) model misspecification, entailing uncertainty about the presence of the true probability model among the set of models considered. Using a new experimental design, we measure individual attitudes towards these different layers of uncertainty and study the distinct role of each of them in characterizing ambiguity attitudes. In addition to providing new insights into the underlying processes behind ambiguity aversion –failure to reduce compound probabilities or distinct attitudes towards unknown probabilities– our study provides the first empirical evidence for the intermediate role of model misspecification between model uncertainty and Ellsberg in decision-makin...

The estimation of the costs of a product or project and the decisions based on these forecasts are subject to much uncertainty relating to factors like unknown future developments. This has been addressed repeatedly in research studies focusing on different aspects of uncertainty; unfortunately, this interest has not yet been adopted in practice. One reason can be found in the inadequate representation of uncertainty. This paper introduces an experiment which engages different approaches to displaying cost forecasting information to gauge the consideration of uncertainty in the subsequent decision-making process. Three different approaches of displaying cost-forecasting information including the uncertainty involved in the data were tested, namely a three point trend forecast, a bar chart and a FAN-diagram. Furthermore, the effects of using different levels of contextual information about the decision problem were examined. The results show that decision makers tend to simplify the level of uncertainty from a possible range of future outcomes to the limited form of a point estimate. Furthermore, the contextual information made the participants more aware of uncertainty. In addition, the fan-diagram prompted 75.0% of the participants to consider uncertainty even if they had not used this type of diagram before; it was therefore identified as the most suitable method of graphical information display for encouraging decision makers to consider the uncertainty in cost forecasting.

RePEc: Research Papers in Economics, 2008

How useful are probabilistic forecasts of the outcomes of particular situations? Potentially, they contain more information than unequivocal forecasts and, as they allow a more realistic representation of the relative likelihood of different outcomes, they might be more accurate and therefore more useful to decision makers. To test this proposition, I first compared a Squared-Error Skill Score (SESS) based on the Brier score with an Absolute-Error Skill Score (AESS), and found the latter more closely coincided with decision-makers' interests. I then analysed data obtained in researching the problem of forecasting the decisions people make in conflict situations. In that research, participants were given lists of decisions that might be made and were asked to make a prediction either by choosing one of the decisions or by allocating percentages or relative frequencies to more than one of them. For this study I transformed the percentage and relative frequencies data into probabilistic forecasts. In most cases the participants chose a single decision. To obtain more data, I used a rule to derive probabilistic forecasts from structured analogies data, and transformed multiple singular forecasts for each combination of forecasting method and conflict into probabilistic forecasts. When compared using the AESS, probabilistic forecasts were not more skilful than unequivocal forecasts.

European Journal of Operational Research, 2011

This paper presents the results of an experiment investigating the effects of using different formats for representing uncertain attribute evaluations on decision making. Study participants make a series of hypothetical choices using six uncertainty formats -probability distributions, expected values, standard deviations, three-point (minimum-median-maximum) approximations, quantiles, and scenarios -and effects on decision making are tracked in terms of the quality of the final choice, the specific characteristics of the selected alternatives, and the difficulty experienced in making a decision. The results provide insights into how subjects make single-and multi-criteria choices in the presence of uncertainty (and some format for representing uncertainty) but in the absence of any real facilitation. The use of probability distributions appeared to overload subjects with information, leading to poorer and more difficult choices than if some intermediate level of summary was usedin particular three-point approximations or quantiles.

Social and Natural Sciences Journal, 2015

The aim of the research was to find out the preferred decision in an economic game. Our sample comprised 433 participants (students of humanities and economics/engineering and customers of betting shops) who were administered this game twice. The wording for the second choice was changed according to two theorems based on this game (Samuelson and Rabin theorems). Our aim was to find out if there were differences in the choice of game between the groups of participants, and if uncertainty and change of wording was related to change in decision. Results show that the most preferred possibility in all three groups of participants was to not play any game. Participants did not make any changes to their decisions even in repeated choice when the wording was changed; they still adhered to their previous choice. Self-assessment of certainty and uncertainty proved significant only in the case of students of humanities (psychology and social work), where assessing the decision as uncertain l...

Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction, 2015

Machine Learning (ML) based decision support systems are often like a black box to non-expert users. Here user's confidence becomes critical for effective decision making and maintaining trust in the system. We find that user confidence varies significantly depending on supplementary material presented on screen. We investigate change in user confidence (in the context of ML based decision making) by varying level of uncertainty presented (in an online water-pipe failure prediction case study) and find that all 26 subjects rated higher uncertainty task to be most difficult and had lowest user confidence in predictive decisions of the same. This agrees with our expectation that increased uncertainty would reduce user confidence in predictive decision making. However, ML-researchers subgroup reported being most confident when uncertainty with known probability was presented, whereas other subgroups (viz. general staff and non-ML researchers) appeared most confident when uncertainty was not at all presented. This is an original research to improve understanding of user's decision making confidence with respect to uncertainty presented in machine learning context.

Frontiers in Neuroscience, 2012

The study of uncertainty in decision-making is receiving greater attention in the fields of cognitive and computational neuroscience. Several lines of evidence are beginning to elucidate different variants of uncertainty. Particularly, risk, ambiguity, and expected and unexpected forms of uncertainty are well articulated in the literature. In this article we review both empirical and theoretical evidence arguing for the potential distinction between three forms of uncertainty; expected uncertainty, unexpected uncertainty, and volatility. Particular attention will be devoted to exploring the distinction between unexpected uncertainty and volatility which has been less appreciated in the literature. This includes evidence mainly from neuroimaging, neuromodulation, and electrophysiological studies. We further address the possible differentiation of cognitive control mechanisms used to deal with these forms of uncertainty. Finally, we explore whether the dual modes of control theory provides a theoretical framework for understanding the distinction between unexpected uncertainty and volatility.